Do Machine Learning Approaches Perform Better Than Regression Models in Mapping Studies? A Systematic Review.

Abstract

Objectives: To identify how machine learning (ML) approaches were implemented in mapping studies and to determine the extent to which ML improved performance compared with regression models (RMs).

Methods: A systematic literature search was conducted in 12 databases from inception to December 2023 to identify studies that applied ML to develop mapping algorithms. A data template was applied to extract data set information, source and target measures, ML approaches and RMs, mapping types (direct vs indirect), goodness-of-fit indicators (mean absolute error, mean squared error, root mean squared error, R-squared, and intraclass correlation coefficient), and validation methods. Differences in goodness-of-fit indicators between ML and RMs were summarized. Potential advantages and challenges for ML were further discussed.

Results: Thirteen mapping studies were identified, in which both ML and RM were adopted. Bayesian networks were the most frequently used ML approach (n = 6), followed by the least absolute shrinkage and selection operator (n = 4). The ordinary least square model was the most used RM (n = 8), followed by the censored least absolute deviation and multinomial logit models (n = 5 each). The average improvement in the goodness-of-fit of ML compared with that of RMs by indicators were 0.007 (mean absolute error), 0.004 (mean squared error), 0.058 (R-squared), 0.016 (intraclass correlation coefficient), and -0.0004 (root mean squared error).

Conclusions: There is an increasing number of studies using ML in developing mapping algorithms. Generally, a minor improvement of goodness-of-fit was observed compared with RMs when using mean-based comparisons. Issues such as how to interpret, apply, and externally validate the ML-based outputs would affect their implementation. Future studies are warranted to verify advantages of ML approaches.